Designing, optimizing and comparing distributed generation technologies as a substitute system for reducing life cycle costs, CO2 emissions, and power losses in residential buildings

Sadeghi, Delnia, Ahmadi, Seyed Ehsan, Amiri, Nima, Satinder, , Marzband, Mousa, Abusorrah, Abdullah and Rawa, Muhyaddin (2022) Designing, optimizing and comparing distributed generation technologies as a substitute system for reducing life cycle costs, CO2 emissions, and power losses in residential buildings. Energy, 253. p. 123947. ISSN 0360-5442

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Official URL: https://doi.org/10.1016/j.energy.2022.123947

Abstract

The optimization of distributed generation technologies and storage systems are essential for a reliable, cost-effective, and secure system due to the uncertainties of Renewable Energy Sources (RESs) and load demand. In this study, two algorithms, the Multi-Objective Particle Swarm Optimization (MOPSO) and the Non-Dominant Sorting Genetic Algorithm II (NSGA-II) were utilized to design five different case studies (CSs) (photovoltaic (PV)/ wind turbine (WT)/ battery/ diesel generator (DG), PV/ WT/ battery/ fuel cell (FC)/ electrolyzer (EL)/ hydrogen tank (HT), PV/ WT/ battery/ grid-connected, PV/ WT/ battery/ grid-connected with Demand Response Program (DRP), and PV/ WT/ battery/ electric vehicle (EV)) to minimize life cycle cost (LCC), loss of power supply probability (LPSP), and CO\text2 emissions. In fact, different backups are provided for (PV/ WT/ battery), which is considered as the base system. Further, the uncertainties in RES and load were modeled by the Taguchi method, and Monte Carlo simulation (MCS) was used to model the uncertainties in EV to achieve accurate results. In addition, in CS4, a Demand Response Program (DRP) based on Time-of-Use (TOU) price is considered to study the effect on the number of specific components and other parameters. Finally, the simulation results verify that the NSGA-II calculation provides accurate and reliable outcomes compared to the MOPSO method, and the PV/WT/battery/ EV combination is the most suitable option among the five designed scenarios.

Item Type: Article
Additional Information: Funding information: The authors acknowledge the support provided by King Abdullah City for Atomic and Renewable Energy (K.A.CARE) under K.A.CARE-King Abdulaziz University Collaboration Program. The authors are also thankful to Deanship of Scientific Research, King Abdulaziz University for providing financial support vide grant number (RG-37-135-42).
Uncontrolled Keywords: demand response program, electric vehicle, Taguchi method, Monte Carlo simulation, Multi-objective particle swarm optimization, non-dominated sorting genetic algorithm II
Subjects: H600 Electronic and Electrical Engineering
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: John Coen
Date Deposited: 06 Apr 2022 12:03
Last Modified: 16 May 2022 09:45
URI: http://nrl.northumbria.ac.uk/id/eprint/48829

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